More than chat, explore your own data with GraphRAG
Retrieval Augmented Generation (RAG) enhances Large Language Models by providing context through an open-source application using txtai, supporting Vector and Graph RAG, and facilitating easy data integration.
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Retrieval Augmented Generation (RAG) is a method designed to enhance the accuracy of Large Language Models (LLMs) by providing them with relevant context through a search query. This article introduces an open-source RAG application built on txtai, which serves as an all-in-one embeddings database for semantic search and LLM orchestration. The application can be run using a Docker container or a Python virtual environment. RAG systems utilize a knowledge base to supply context, which is crucial when dealing with large volumes of documents. The application supports two types of RAG: Vector RAG, which uses vector searches to find relevant matches, and Graph RAG, which employs semantic graphs for context generation. The article also details the text extraction process, which is essential for loading data into the embeddings database. The Textractor pipeline is highlighted for its ability to extract and format text from various document types, ensuring that only relevant content is processed. The application is designed for ease of use, allowing users to quickly explore and integrate their custom data. Overall, the RAG application with txtai offers a streamlined approach to improving LLM responses by grounding them in factual information.
- RAG reduces hallucinations in LLMs by providing relevant context.
- The application can be run via Docker or Python virtual environments.
- It supports both Vector RAG and Graph RAG for context generation.
- The Textractor pipeline enhances text extraction and formatting.
- Users can easily integrate their own data into the embeddings database.
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